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Related Experiment Video

Updated: Jul 7, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Euclidean distance transform for binary images on reconfigurable mesh-connected computers.

Y Pan¹, M Hamdi, K Li

¹Dept. of Comput. Sci., Dayton Univ., OH.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This study presents a faster parallel algorithm for Euclidean distance transform (EDT) calculations in images. The new algorithm achieves O(log(2)n) time complexity on reconfigurable meshes, significantly improving upon previous O(n) methods.

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Area of Science:

Computer Vision
Parallel Algorithms
Robotics

Background:

Euclidean distance transform (EDT) is fundamental in image analysis.
Existing parallel algorithms for EDT have limitations in speed.
Chen and Chuang's 1995 algorithm runs in O(n) time on mesh-connected SIMD computers.

Purpose of the Study:

To develop a more efficient parallel algorithm for computing the EDT.
To improve the time complexity of EDT calculation on mesh-connected architectures.

Main Methods:

Proposed a novel parallel algorithm for EDT computation.
Utilized a reconfigurable mesh model for implementation.
Analyzed algorithm performance on a 2-D nxn reconfigurable mesh.

Main Results:

Achieved a time complexity of O(log(2)n) for EDT calculation.
The algorithm demonstrates significant improvement over prior O(n) methods.
The reconfigurable mesh offers comparable VLSI area efficiency to plain meshes.

Conclusions:

The proposed algorithm offers a substantial speedup for parallel EDT computation.
Reconfigurable meshes are an efficient architecture for advanced image processing tasks.
This work advances the field of parallel image analysis and computer vision.